Mort's Report — Sports Data Analytics
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Week 3 · Sun, 4:25 PM EDT · CBS

Baltimore Ravens at Dallas Cowboys

Neutral site · Maracanã Stadium, Rio De Janeiro

Baltimore Ravens
Ravens
BAL
8-9 · #10
59%
BAL to win
BAL -2.5 · Lean
Dallas Cowboys
Cowboys
DAL
7-9-1 · #27

Market line for comparison: BAL -3 · O/U 52.5 (DraftKings). The model never sees this.

Rating inputs

Each starting rating is a weighted blend of five real football inputs, not just wins and losses. Every term below is measured from the 2025 season.

Baltimore Ravens

InputRatingWeightContribution
Elo from results
8-9 in 2025, opponent-adjusted
152380%1218.8
Scoring strength
424–398 points (+26.0 differential) → 53.7% expected win rate
15265%76.3
Late-season form
same replay with the closing 6 weeks counted double
153315%229.9
Offense / defense split
24.9 PPG scored, 23.4 allowed → offense 1548, defense 1490
15380%0.0
Blended 2025 rating1525.0
Offseason regression
25% back toward the 1500 league mean
1518.7
Roster / QB adjustment
No offseason adjustment applied.
0.0
BAL 2026 starting rating1519

Dallas Cowboys

InputRatingWeightContribution
Elo from results
7-9-1 in 2025, opponent-adjusted
143780%1149.9
Scoring strength
471–511 points (-40.0 differential) → 45.2% expected win rate
14665%73.3
Late-season form
same replay with the closing 6 weeks counted double
138615%207.9
Offense / defense split
27.7 PPG scored, 30.1 allowed → offense 1617, defense 1324
14410%0.0
Blended 2025 rating1431.1
Offseason regression
25% back toward the 1500 league mean
1448.3
Roster / QB adjustment
No offseason adjustment applied.
0.0
DAL 2026 starting rating1448

The calculation

Every term the model applies, in order, from both teams' starting ratings to the number on the card.

Dallas Cowboys base rating
1474.4
2026 starting Elo, after offseason regression
Baltimore Ravens base rating
− 1511.1
2026 starting Elo, after offseason regression
Rating edge (home perspective)
−36.7
BAL is the better-rated side on neutral ground
Home-field advantage
+0.0
Neutral site (Rio De Janeiro) — no home-field points applied
Rest differential
+0.0
Both teams open the season equally rested
Analyst override
+0.0
No manual adjustment on this game
Injury adjustment
−30.0
DAL is the more banged-up side — see the injury breakdown below
Adjusted Elo difference
−66.7

Win probability

1 / (1 + 10−(-66.7 / 400))

59.5% BAL

Model spread

-66.7 ÷ 25, rounded to the nearest half point

BAL -2.5

Confidence band

Coin flip · under 55%Lean · 55–65%Confident · 65–75%Strong · 75% and up

Injury adjustment

Report pulled 2026-10-27 13:20 UTC

Ratings describe a team over a whole season. Who is actually available on Sunday is a separate question, so the injury adjustment is applied last, after home field and rest. Each listed player costs a share of a point of spread based on his position, whether he starts, and how likely he is to play.

Before injuries

55.3%

BAL by 1.5

Injury term

−30.0 Elo

−1.2 pts of spread, home perspective

After injuries

59.5%

BAL by 2.5 · moved 4.2 pts of probability

Baltimore Ravens

−0.5 pts

  • Tavius Robinson LBOut−0.10
  • Jaylinn Hawkins SOut−0.10
  • Broderick Washington Jr. DTOut−0.10
  • T.J. Tampa CBOut−0.10

Dallas Cowboys

−0.3 pts

  • P.J. Locke SOut−0.10
  • Marist Liufau LBOut−0.10
  • KaVontae Turpin WRQuestionable−0.03
  • Dee Winters LBQuestionable−0.03

Statuses come straight from the league injury report: out and injured reserve count in full, doubtful three quarters, questionable roughly a third, probable a tenth. Starters are identified from the depth chart; anyone the model cannot place is treated as a backup.

Move the factors yourself

Every term feeds one number: the adjusted Elo difference. Drag any of them and watch the spread and confidence follow.

1474

Drag to see how much a stronger or weaker host changes the call.

1511

The two ratings only matter through their difference.

+0 pts

Neutral site, so the model applies 0. The tuned value elsewhere is 25.

+0 pts

5 Elo per extra day of rest, capped at 25 either way.

+0 pts

A manual thumb on the scale, in the home team's direction.

Win probability

55.3% BAL

Model value

Model spread

BAL -1.5

Elo diff −36.7 · Lean

Probability curve

The logistic curve from −600 to +600 Elo, home perspective. The dot is your current setting; the faint vertical line is where the model itself sits. Notice the curve is flattest at the extremes — near a coin flip, every point of Elo moves the number most.

Where the ratings came from

Each rating is the end state of a full 2025 season of Elo updates, regressed toward the league mean for the new season.

Baltimore Ravens
Baltimore Ravens
#10 of 32 · 8-9 in 2025
2025 final rating
1523.5
Regress 25% toward 1500
−4.8
2026 starting rating
1518.7

2025 Elo trail

Biggest swings

  • L 14–32 vs CIN−67.5
  • W 30–16 vs CHI+49.8
  • L 10–44 vs HOU−49.0
Dallas Cowboys
Dallas Cowboys
#27 of 32 · 7-9-1 in 2025
2025 final rating
1437.3
Regress 25% toward 1500
+11.0
2026 starting rating
1448.3

2025 Elo trail

Biggest swings

  • W 44–22 vs WSH+46.9
  • L 14–31 at CHI−44.9
  • L 17–34 at NYG−41.6

Matchup factors

Everything beyond the two base ratings that moved this specific number.

Home field

Neutral

Played at Maracanã Stadium, Rio De Janeiro. Neither side gets the 25-point host bonus.

Rest

+0.0 pts

Both teams come in on equal rest, so there is no rest edge to give.

Analyst override

+0.0 pts

No manual adjustment. The number here is pure model output.

2025 head to head

These teams did not meet in 2025. Past meetings carry no weight in the model anyway — they are already baked into each team's rating.

Strength of schedule

Who each team actually played in 2025, measured by their opponents' current ratings. This is context for reading the ratings — it is not an input to the prediction above, because beating good teams already raises a team's Elo more.

Baltimore Ravens

1506

average opponent rating · 11th toughest of 32

vs above-average teams
2-6
vs below-average teams
6-3
Dallas Cowboys

1476

average opponent rating · 31st toughest of 32

vs above-average teams
1-5
vs below-average teams
6-4-1

What the model does not know

  • · How an injury actually plays out — the adjustment above prices availability, not how well a replacement performs.
  • · Coaching and roster turnover between seasons.
  • · Weather, travel distance and short-week fatigue beyond raw rest days.
  • · Anything a betting market has priced in since the season ended.
Read the full methodology →